Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf, Alexander J. Smola, Managing Director of the Max Planck Institute for Biological Cybernetics in Tubingen Germany Profe Bernhard Scholkopf
MIT Press, 2002 - Computers - 626 pages
A comprehensive introduction to Support Vector Machines and related kernel methods.
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs---kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.
Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
What people are saying - Write a review
Once you master this book, no doubt you will be an expert in kernel-based learning methods. From my experience, those readers with no math background need a strong patience to consume the equations explained.
Also note, that reading and understanding the book without solving the problems at the end of each chapter is not the best way to learn. Solve every problem.
My regards to the authors.
You can't really understand modern supervised machine learning until you've mastered the techniques in this book.
A Tutorial Introduction
CONCEPTS AND TOOLS
Risk and Loss Functions
Elements of Statistical Learning Theory
SUPPORT VECTOR MACHINES
Quantile Estimation and Novelty Detection
Kernel Feature Extraction
Kernel Fisher Discriminant
Bayesian Kernel Methods
Regularized Principal Manifolds
PreImages and Reduced Set Methods
B Mathematical Prerequisites